Circulating tumor cell-derived exosome-transmitted long non-coding RNA TTN-AS1 can promote the proliferation and migration of cholangiocarcinoma cells.

BACKGROUND: Exosomes assume a pivotal role as essential mediators of intercellular communication within tumor microenvironments. Within this context, long noncoding RNAs (LncRNAs) have been observed to be preferentially sorted into exosomes, thus exerting regulatory control over the initiation and progression of cancer through diverse mechanisms. RESULTS: Exosomes were successfully isolated from cholangiocarcinoma (CCA) CTCs organoid and healthy human serum. Notably, the LncRNA titin-antisense RNA1 (TTN-AS1) exhibited a conspicuous up-regulation within CCA CTCs organoid derived exosomes. Furthermore, a significant elevation of TTN-AS1 expression was observed in tumor tissues, as well as in blood and serum exosomes from patients afflicted with CCA. Importantly, this hightened TTN-AS1 expression in serum exosomes of CCA patients manifested a strong correlation with both lymph node metastasis and TNM staging. Remarkably, both CCA CTCs organoid-derived exosomes and CCA cells-derived exosomes featuring pronounced TTN-AS1 expression demonstrated the capability to the proliferation and migratory potential of CCA cells. Validation of these outcomes was conducted in vivo experiments. CONCLUSIONS: In conclusion, our study elucidating that CCA CTCs-derived exosomes possess the capacity to bolster the metastasis tendencies of CCA cells by transporting TTN-AS1. These observations underscore the potential of TTN-AS1 within CTCs-derived exosomes to serve as a promising biomarker for the diagnosis and therapeutic management of CCA.

Prediction of small molecule drug-miRNA associations based on GNNs and CNNs.

MicroRNAs (miRNAs) play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules (SMs). Due to the time-consuming and expensive biological experiments required to validate SM-miRNA associations, there is an urgent need to develop new computational models to predict novel SM-miRNA associations. The rapid development of end-to-end deep learning models and the introduction of ensemble learning ideas provide us with new solutions. Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule association prediction model (GCNNMMA). Firstly, we use GNNs to effectively learn the molecular structure graph data of small molecule drugs, while using CNNs to learn the sequence data of miRNAs. Secondly, since the black-box effect of deep learning models makes them difficult to analyze and interpret, we introduce attention mechanisms to address this issue. Finally, the neural attention mechanism allows the CNNs model to learn the sequence data of miRNAs to determine the weight of sub-sequences in miRNAs, and then predict the association between miRNAs and small molecule drugs. To evaluate the effectiveness of GCNNMMA, we implement two different cross-validation (CV) methods based on two different datasets. Experimental results show that the cross-validation results of GCNNMMA on both datasets are better than those of other comparison models. In a case study, Fluorouracil was found to be associated with five different miRNAs in the top 10 predicted associations, and published experimental literature confirmed that Fluorouracil is a metabolic inhibitor used to treat liver cancer, breast cancer, and other tumors. Therefore, GCNNMMA is an effective tool for mining the relationship between small molecule drugs and miRNAs relevant to diseases.